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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Lin, Haocai Pan, Jiacheng Dong, Yihong Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In the medical imaging field, computer-Aided Detection (CADe) has greatly benefited from the recent development of Graph Convolutional Networks (GCNs). GCN-based predictive models require building a population graph to detect the disease states of each subject, based on imaging and non-imaging data. Until now, all existing population-level methods are homogeneous, failing to consider sex differences. To address this issue, we present a heterogeneous population graph convolutional network with hierarchical attention mechanisms, including intra-level and inter-level attention. Specifically, the intra-level attention layer is aimed at learning differences and similarities between the sexes, while the inter-level attention layer is responsible for information integration by assigning weights to different features. The objective is to obtain node embeddings describing individual characteristics completely and provide discriminative inputs to classifiers. Compared to benchmark models, our proposal achieves satisfying prediction results on three datasets, illustrating the framework's ability to extract predictive attributes from medical multimodal data. © 2022 IEEE.

关键词： Heterogeneous networks

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A Comparative Analysis of Feature Selection Methods on the Accuracy of Heart Disease Prediction Models

A Comparative Analysis of Feature Selection Methods on the A...

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2024 International Conference on Recent Progresses in science, engineering and Technology, ICRPSET 2024

作者： Salehin, Sultanus Islam, Akib Jayed Iqbal, Azmain Mahpara Barua, Labannya Islam, Kamrul Uddin, Arafat The University of Alabama Electrical and Computer Engineering Tuscaloosa United States Norwegian University of Science and Technology Computational Colour & Spectral Imaging Gjovik Norway Universiti Teknologi Malaysia Software Engineering Johor Malaysia Premier University Computer Science and Engineering Chittagong Bangladesh The University of Alabama Electrical & Computer Engineering Tuscaloosa United States Port City International University Electrical & Electronic Engineering Chattogram Bangladesh

ISBN: (纸本)9798331509477

Heart disease is a leading cause of death globally, making accurate diagnostic methods crucial. This study explores the use of machine learning, particularly artificial intelligence, for heart disease prediction, addressing challenges posed by the high dimensionality of medical datasets. By comparing feature selection techniques, including Wrapper and Filter methods and expert-driven approaches, the study identifies the most relevant features for prediction. It uses two widely recognized datasets: the Cleveland Heart Disease dataset and the Framingham Heart Study dataset. These datasets, while both aimed at heart disease prediction, differ in their feature sets and target populations. The goal is to reduce the number of features while maintaining prediction accuracy. Various machine learning classifiers - SVM, Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), and Adaptive Boosting (AdaBoost) - were applied to both the full and reduced feature sets. Feature selection methods such as Sequential Forward and Backward Selection, Chi-Square, and Mutual Information were tested for effectiveness. The study shows that feature reduction improves classification accuracy and reduces training time. The comparison of the Cleveland and Framingham datasets reveals how each dataset's characteristics impact the performance of feature selection methods and heart disease prediction, highlighting the potential for generalizing across different datasets. © 2024 IEEE.

关键词： Accuracy Cardiovascular diseases (CVDs) Chi-square Cleveland Heart Disease dataset Feature selection Framingham Heart Study dataset Machine learning Medical knowledge-driven feature selection Mutual index Precision

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MQTT-Enabled Smart Door Access System: Design and Implementation Using NodeMCU ESP 8266 and HiveMQ 9

MQTT-Enabled Smart Door Access System: Design and Implementa...

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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Zainuddin, Ahmad Anwar Nor, Rizal Mohd Hussin, Amir 'Aatieff Amir Sazali, Muhammad Nurzikry Mohd International Islamic University Malaysia Kuliyyah of ICT Department of Computer Science Kuala Lumpur Malaysia International Islamic University Malaysia Kuliyyah of Engineering Department of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350370126

The security of one's residence or property is an essential consideration that warrants careful attention to safeguard the premises and its contents. It has become increasingly apparent today that physical key door locks are susceptible to damage and can be compromised by unauthorized individuals. The inherent vulnerability of physical key door locks stems from their susceptibility to misplaced keys and duplication, as individuals are required to always carry them. This work presents a novel Internet of Things (IoT) enabled smart door access system designed to improve the security of homes or premises. The system offers keyless access to individuals who have been granted authorization. The system's architectural design incorporates the utilization of a NodeMCU ESP 8266 microcontroller to oversee door operations. Access control functionality is facilitated through the implementation of the Virtuino IoT application, which enables the unlocking of doors using smartphones. Additionally, a MQTT broker, specifically the HiveMQ, is implemented to facilitate effective machine-to-machine communication within the IoT components. The rationale behind the adoption of this IoT-based solution stems from the inherent weaknesses of traditional physical key door locks, which are prone to damage and replication, consequently jeopardizing security measures. The proposed system aims to address these limitations by rendering traditional keys obsolete, thereby improving both security and convenience for users. The system has undergone comprehensive development, testing, and operationalization, resulting in a highly functional system. The success rate of all functions has consistently achieved a 100% level, which serves as an indication of the system's robustness and effectiveness. In addition, the practical implementation of the system in the Centre of Excellence for Cybersecurity (CoExCys) office at Kuliyyah of ICT IIUM exhibited smooth operation and the absence of any operational chall

关键词： Locks (fasteners)

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks 3

Agricultural Water Conservation through AI-Enhanced Solar Io...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Maniraj, S.P. Monisha, J. Amutha, R. Kavitha, R. Shriidhar, P.J. Srinivasan, C. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur Tamilnadu Chennai India Tamil Nadu Chennai India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Universityi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future. © 2025 IEEE.

关键词： Smart agriculture

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Development of a Chipless RFID Water Leakage Detecting Sensor Using Defected Ground Structure

Development of a Chipless RFID Water Leakage Detecting Senso...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Weng, Weiming Wang, Jian Tong, Kun Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9781665452366

In this paper, a fully printable low-cost chipless RFID water leakage detecting sensor is presented. The sensor comprises of 3 λ/4 stubs to encode identity information. Each λ/4 stub represents a single data bit, thus 3 bits of data can be stored. The integration of pipeline water leakage sensing is performed by utilizing the resonant frequency of a bullet-slot DGS attached with a sponge. If the water pipe leaks because of cracks or failure of socket joint, the sponge absorbs water and affects the resonate characteristic of DGS. And the identity information of the sensor is guaranteed by the natural ground of λ/4 stubs. Experiment results show that the sensor has a high sensitivity to water, and has the advantages of independent control of sensing information and identity information. © 2022 IEEE.

关键词： Natural frequencies

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Insight into Janus V2COS as anode material of high-performance alkali metal ion battery: Diffusion barrier, recyclability, specific capacity, and open-circuit voltage

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Physical Review Materials 2024年第8期8卷 085801-085801页

作者： Fanfan Wang Jun Yuan Zhufeng Zhang Baoan Song Junhua Zhao Jincheng Yue Tian Xu Jun Zhou School of Physical Science and Technology Faculty of Electrical Engineering and Computer Science College of Chemical and Material Engineering School of Electronic Information Engineering

Transition metal carbides, nitrides, and carbonitrides, known as MXenes, exhibit exceptional conductivity, stability, and large specific surface area, rendering them promising candidates for anode materials in rechargeable batteries. Herein, we investigate the electrochemical characteristics of the Janus MXene V2COS monolayer, as an anode material of alkali metal ion batteries by using first-principles calculations. The phonon band structure and ab initio molecular dynamics simulations confirm the stability of the Janus V2COS monolayer. The mechanical and electrical properties of the Janus V2COS monolayer are explored and proved to have good mechanical stability and electrical conductivity. The surface of the Janus V2COS monolayer demonstrates the facile adsorption of alkali metal ions and low diffusion barriers. As an anode material, the recyclability of the Janus V2COS has been verified in the ion intercalation/deintercalation processes. Furthermore, the theoretical specific capacities and the open-circuit voltages of the Janus V2COS monolayer are calculated to be 165.54mAh/g and 2.62 V for Li, 662.18mAh/g and 0.76 V for Na, and 294.43mAh/g and 0.4 V for K, respectively. It presents that the Janus V2COS monolayer is a potential anode material of sodium-ion batteries and potassium-ion batteries.

关键词： Electrochemical properties 2-dimensional systems Density functional theory First-principles calculations

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"Fiction to Function"Shaping Renewable Energy Education with MATLAB and ChatGPT-Driven Environments 21

"Fiction to Function"Shaping Renewable Energy Education with...

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21st International Learning and Technology Conference, L and T 2024

作者： Mohamed, Baher Babiker, Safia Aldybous, Alaa Kabbaj, Narjisse Brahimi, Tayeb Effat University Effat College of Engineering Electrical and Computer Engineering Department Jeddah Saudi Arabia Effat University Effat College of Engineering Energy and Technology Research Center Electrical and Computer Engineering Department Jeddah Saudi Arabia Effat University Natural Science Math and Tech Unit Effat College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Addressing the gap in effective and engaging renewable energy education, this study, 'Fiction to Function,' transitions imaginative frameworks into practical educational tools, focusing on solar and wind energy in line with Sustainable Development Goals (SDGs). The goal is to empower users with deep insights into renewable energy through the novel integration of MATLAB and ChatGPT-driven environments, enhancing the analysis and exploration of these energy sources. The study employs a structured approach using MATLAB App Designer to develop a user-friendly GUI, aligning instructional content with the SDGs and integrating ChatGPT via the OpenAI API for dynamic, interactive learning experiences. This integration provides a multifaceted platform that not only educates but also engages users in practical scenarios, bridging the gap between theoretical knowledge and real-world application. The results demonstrate an enhanced understanding of renewable energy concepts among users, showcasing the efficacy of combining MATLAB's analytical capabilities with ChatGPT's AI-driven guidance. Ultimately, the study highlights the transformative potential of innovative technology in sustainable energy education, setting a precedent for future educational tools in this domain. © 2024 IEEE.

关键词： Integration

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Hyperbolic Distance Based on EMD and Diffusion for Hyperspectral Imaging

Hyperbolic Distance Based on EMD and Diffusion for Hyperspec...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Elad Lavi Amir Bourvine Ya-Wei Eileen Lin Ronen Talmon Taub Faculty of Computer Science Technion Viterbi Faculty of Electrical and Computer Engineering Technion

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we introduce EMD-Based Hyperbolic Diffusion Distance (EMD-HDD), a new method for constructing a meaningful distance metric for hierarchical data with latent hierarchical structure. Our method relies on hyperbolic geometry, diffusion geometry, and the Earth Mover’s Distance (EMD). Specifically, our method embeds data points into a product manifold of hyperbolic spaces, allowing us to recover the hidden hierarchical structure encoded by the mutual relationships between features. We demonstrate the effectiveness of EMD-HDD through experiments on five hyperspectral imaging datasets, showcasing its capability to capture and reveal the intrinsic hierarchical structures inherent in such data.

关键词： Manifolds Geometry Measurement Earth Imaging Signal processing Acoustics Speech processing Hyperspectral imaging

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An Information-Theoretic Evaluation of Generative Models in Learning Multi-modal Distributions 37

An Information-Theoretic Evaluation of Generative Models in ...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Jalali, Mohammad Li, Cheuk Ting Farnia, Farzan Department of Electrical and Computer Engineering Isfahan University of Technology Israel Department of Information Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781713899921

The evaluation of generative models has received significant attention in the machine learning community. When applied to a multi-modal distribution which is common among image datasets, an intuitive evaluation criterion is the number of modes captured by the generative model. While several scores have been proposed to evaluate the quality and diversity of a model's generated data, the correspondence between existing scores and the number of modes in the distribution is unclear. In this work, we propose an information-theoretic diversity evaluation method for multi-modal underlying distributions. We utilize the Rényi Kernel Entropy (RKE) as an evaluation score based on quantum information theory to measure the number of modes in generated samples. To interpret the proposed evaluation method, we show that the RKE score can output the number of modes of a mixture of sub-Gaussian components. We also prove estimation error bounds for estimating the RKE score from limited data, suggesting a fast convergence of the empirical RKE score to the score for the underlying data distribution. Utilizing the RKE score, we conduct an extensive evaluation of state-of-the-art generative models over standard image datasets. The numerical results indicate that while the recent algorithms for training generative models manage to improve the mode-based diversity over the earlier architectures, they remain incapable of capturing the full diversity of real data. Our empirical results provide a ranking of widely-used generative models based on the RKE score of their generated samples. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Quality control

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Impact of temporal Context on Sound Event Detection in Complex Head Related Impulse Response Acoustic Scenes. 24

Impact of temporal Context on Sound Event Detection in Compl...

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24th International Congress on Acoustics, ICA 2022

作者： Adewusi, Adeoluwawale Obermayer, Klaus Department of Electrical Engineering and computer Science Technische Universitat Berlin Germany

Temporal context is an inherent property of audio event that describes the energy activity within an audio sample over entire duration of the sample. The perception of sound sources in polyphonic acoustic scenes is hinged on the temporal and spatial signatures of foreground and background sources in these scenes. Presently, the exact temporal size of audio signal that is required to predict an audio event in acoustic scene is not known. Thus, this work attempts to model the sufficient length of sound event data that sound classifiers can access towards robust audio event detection in complex acoustic scenes. Complex binaural acoustic scenes are simulated by convolving audio from sound events classes with head-related room impulse responses under diverse acoustic conditions such as source azimuths and signal to noise ratio. Furthermore, deep learning-based sound classifiers are trained with varying temporal window lengths of scene data using multi-conditional training approach. Moreover, the influence of acoustic conditions on sound classification models' strength with different temporal context lengths are evaluated. The results reveal how temporal context length and these acoustic scene conditions affect the generalization performance of sound event classifiers in complex audio scenes. © 2022 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Impulse response

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